Back

Diagnostic Accuracy of Preoperative C-reactive protein/albumin ratio for Predicting Postoperative Delirium: A Protocol for a Systematic Review and Diagnostic Test Accuracy Meta-analysis

Mihara, T.; Okazaki, M.; Koto, R.; Noma, H.; Sasaki, K.

2025-10-31 anesthesia
10.1101/2025.10.29.25339103 medRxiv
Show abstract

BackgroundPostoperative delirium (POD) is a frequent and severe complication associated with increased morbidity, mortality, and healthcare costs. Effective preoperative risk stratification is crucial for prevention. The C-reactive protein/albumin ratio (CAR), an accessible biomarker that reflects both systemic inflammation and nutritional status, shows promise for POD prediction. However, the overall diagnostic performance across diverse surgical populations has not yet been quantified. This study aims to systematically review and synthesize evidence on the diagnostic accuracy of preoperative CAR in predicting POD. MethodsThis protocol will adhere to the PRISMA-DTA statement. A comprehensive search will be conducted in major electronic databases, including MEDLINE, Embase, and Cochrane Library, without language restrictions. Observational studies and clinical trials evaluating the accuracy of preoperative CAR in predicting POD in adult surgical patients, where POD is diagnosed using a validated tool, will be included. Two reviewers will independently perform study screening, data extraction, and risk of bias assessment using the QUADAS-2 tool. For the primary analysis, a bivariate random-effects model will be used to synthesize true-positive, false-positive, true-negative, and false-negative data, generating coupled forest plots and a hierarchical summary receiver operating characteristic (sROC) curve. Heterogeneity will be quantified, and its sources will be explored through pre-specified subgroup and sensitivity analyses. Ethics and DisseminationAs a systematic review of previously published data, formal ethical approval is not required. The findings will be disseminated through publication in a peer-reviewed journal and presentation at scientific conferences.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.